
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots


races_data = pd.read_csv('races.csv')
diverstanding_data = pd.read_csv('driver_standings.csv')
drivers_data = pd.read_csv('drivers.csv')
constructorstanding_data = pd.read_csv('constructor_standings.csv')
constructors_data = pd.read_csv('constructors.csv')
constructor_race_data = pd.read_csv('constructor_results.csv')


data_2011 = races_data.where(races_data['year']==2011).dropna()
data_2011 = data_2011.merge(diverstanding_data,on='raceId').dropna()
data_2011 = data_2011.merge(drivers_data,on='driverId').dropna()
data_2011.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2011 = data_2011.sort_values(['round','position'])
data_2011


data_teams_2011 = races_data.where(races_data['year']==2011).dropna()
data_teams_2011 = data_teams_2011.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2011 = data_teams_2011.merge(constructors_data,on='constructorId').dropna()
data_teams_2011.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2011 = data_teams_2011.sort_values(['round','position'])
data_teams_2011.head(30)


#teams_color_2011 = {'Red Bull Racing':dict(color='darkblue'),
#                   'McLaren':dict(color='orange'),
#                    'Ferrari':dict(color='red'),
#                    'Mercedes':dict(color='teal'),
#                    'Renault':dict(color='yellow'),
#                    'Williams':dict(color='')
#                   }


#drivers = pd.DataFrame()
constructors = data_teams_2011['name_y'].unique()
drivers = data_2011['code'].unique()
#drivers['color'] = []


fig1 = go.Figure()
fig2 = go.Figure()

for i in drivers:
    d = data_2011.where(data_2011['code'] == i).dropna()
    fig1.add_trace(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig2.add_trace(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig1.show()


fig2['layout']['yaxis']['autorange'] = "reversed"
fig2.show()


fig3 = go.Figure()
fig4 = go.Figure()

for i in constructors:
    d = data_teams_2011.where(data_teams_2011['name_y'] == i).dropna()
    fig3.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig4.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig3.show()


fig4['layout']['yaxis']['autorange'] = "reversed"
fig4.show()


data_2012 = races_data.where(races_data['year']==2012).dropna()
data_2012 = data_2012.merge(diverstanding_data,on='raceId').dropna()
data_2012 = data_2012.merge(drivers_data,on='driverId').dropna()
data_2012.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2012 = data_2012.sort_values(['round','position'])
data_2012.head(30)

drivers = data_2012['code'].unique()


fig5 = go.Figure()
fig6 = go.Figure()
for i in drivers:
    d = data_2012.where(data_2012['code'] == i).dropna().sort_values(by='round') 
    fig5.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig6.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig5.show()


fig6['layout']['yaxis']['autorange'] = "reversed"
fig6.show()


data_teams_2012 = races_data.where(races_data['year']==2012).dropna()
data_teams_2012 = data_teams_2012.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2012 = data_teams_2012.merge(constructors_data,on='constructorId').dropna()
data_teams_2012.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2012 = data_teams_2012.sort_values(['round','position'])
constructors = data_teams_2012['name_y'].unique()
data_teams_2012


fig7 = go.Figure()
fig8 = go.Figure()

for i in constructors:
    d = data_teams_2012.where(data_teams_2012['name_y'] == i).dropna()
    fig7.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig8.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig7.show()


fig8['layout']['yaxis']['autorange'] = "reversed"
fig8.show()


data_2013 = races_data.where(races_data['year']==2013).dropna()
data_2013 = data_2013.merge(diverstanding_data,on='raceId').dropna()
data_2013 = data_2013.merge(drivers_data,on='driverId').dropna()
data_2013.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2013 = data_2013.sort_values(['round','position'])
data_2013
drivers = data_2013['code'].unique()


fig9 = go.Figure()
fig10 = go.Figure()
for i in drivers:
    d = data_2013.where(data_2013['code'] == i).dropna().sort_values(by='round') 
    fig9.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig10.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig9.show()


fig10['layout']['yaxis']['autorange'] = "reversed"
fig10.show()


data_teams_2013 = races_data.where(races_data['year']==2013).dropna()
data_teams_2013 = data_teams_2013.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2013 = data_teams_2013.merge(constructors_data,on='constructorId').dropna()
data_teams_2013.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2013 = data_teams_2013.sort_values(['round','position'])
constructors = data_teams_2013['name_y'].unique()
data_teams_2013


fig11 = go.Figure()
fig12 = go.Figure()

for i in constructors:
    d = data_teams_2013.where(data_teams_2013['name_y'] == i).dropna()
    fig11.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig12.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig11.show()


fig12['layout']['yaxis']['autorange'] = "reversed"
fig12.show()


data_2014 = races_data.where(races_data['year']==2014).dropna()
data_2014 = data_2014.merge(diverstanding_data,on='raceId').dropna()
data_2014 = data_2014.merge(drivers_data,on='driverId').dropna()
data_2014.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2014 = data_2014.sort_values(['round','position'])
data_2014
drivers = data_2014['code'].unique()


fig13 = go.Figure()
fig14 = go.Figure()
for i in drivers:
    d = data_2014.where(data_2014['code'] == i).dropna().sort_values(by='round') 
    fig13.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig14.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig13.show()


fig14['layout']['yaxis']['autorange'] = "reversed"
fig14.show()


data_teams_2014 = races_data.where(races_data['year']==2014).dropna()
data_teams_2014 = data_teams_2014.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2014 = data_teams_2014.merge(constructors_data,on='constructorId').dropna()
data_teams_2014.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2014 = data_teams_2014.sort_values(['round','position'])
constructors = data_teams_2014['name_y'].unique()
data_teams_2014


fig15 = go.Figure()
fig16 = go.Figure()

for i in constructors:
    d = data_teams_2014.where(data_teams_2014['name_y'] == i).dropna()
    fig15.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig16.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig15.show()


fig16['layout']['yaxis']['autorange'] = "reversed"
fig16.show()


data_2015 = races_data.where(races_data['year']==2015).dropna()
data_2015 = data_2015.merge(diverstanding_data,on='raceId').dropna()
data_2015 = data_2015.merge(drivers_data,on='driverId').dropna()
data_2015.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2015 = data_2015.sort_values(['round','position'])
data_2015
drivers = data_2015['code'].unique()


fig17 = go.Figure()
fig18 = go.Figure()
for i in drivers:
    d = data_2015.where(data_2015['code'] == i).dropna().sort_values(by='round') 
    fig17.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig18.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig17.show()


fig18['layout']['yaxis']['autorange'] = "reversed"
fig18.show()


data_teams_2015 = races_data.where(races_data['year']==2015).dropna()
data_teams_2015 = data_teams_2015.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2015 = data_teams_2015.merge(constructors_data,on='constructorId').dropna()
data_teams_2015.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2015 = data_teams_2015.sort_values(['round','position'])
constructors = data_teams_2015['name_y'].unique()
data_teams_2015


fig19 = go.Figure()
fig20 = go.Figure()

for i in constructors:
    d = data_teams_2015.where(data_teams_2015['name_y'] == i).dropna()
    fig19.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig20.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig19.show()


fig20['layout']['yaxis']['autorange'] = "reversed"
fig20.show()


data_2016 = races_data.where(races_data['year']==2016).dropna()
data_2016 = data_2016.merge(diverstanding_data,on='raceId').dropna()
data_2016 = data_2016.merge(drivers_data,on='driverId').dropna()
data_2016.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2016 = data_2016.sort_values(['round','position'])
data_2016
drivers = data_2016['code'].unique()


fig21 = go.Figure()
fig22 = go.Figure()
for i in drivers:
    d = data_2016.where(data_2016['code'] == i).dropna().sort_values(by='round') 
    fig21.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig22.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig21.show()


fig22['layout']['yaxis']['autorange'] = "reversed"
fig22.show()


data_teams_2016 = races_data.where(races_data['year']==2016).dropna()
data_teams_2016 = data_teams_2016.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2016 = data_teams_2016.merge(constructors_data,on='constructorId').dropna()
data_teams_2016.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2016 = data_teams_2016.sort_values(['round','position'])
constructors = data_teams_2016['name_y'].unique()
data_teams_2016


fig23 = go.Figure()
fig24 = go.Figure()

for i in constructors:
    d = data_teams_2016.where(data_teams_2016['name_y'] == i).dropna()
    fig23.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig24.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig23.show()


fig24['layout']['yaxis']['autorange'] = "reversed"
fig24.show()


data_2017 = races_data.where(races_data['year']==2017).dropna()
data_2017 = data_2017.merge(diverstanding_data,on='raceId').dropna()
data_2017 = data_2017.merge(drivers_data,on='driverId').dropna()
data_2017.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2017 = data_2017.sort_values(['round','position'])
data_2017
drivers = data_2017['code'].unique()


fig25 = go.Figure()
fig26 = go.Figure()
for i in drivers:
    d = data_2017.where(data_2017['code'] == i).dropna().sort_values(by='round') 
    fig25.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig26.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig25.show()


fig26['layout']['yaxis']['autorange'] = "reversed"
fig26.show()


data_teams_2017 = races_data.where(races_data['year']==2017).dropna()
data_teams_2017 = data_teams_2017.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2017 = data_teams_2017.merge(constructors_data,on='constructorId').dropna()
data_teams_2017.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2017 = data_teams_2017.sort_values(['round','position'])
constructors = data_teams_2017['name_y'].unique()
data_teams_2017


fig27 = go.Figure()
fig28 = go.Figure()

for i in constructors:
    d = data_teams_2017.where(data_teams_2017['name_y'] == i).dropna()
    fig27.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig28.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig27.show()


fig28['layout']['yaxis']['autorange'] = "reversed"
fig28.show()


data_2018 = races_data.where(races_data['year']==2018).dropna()
data_2018 = data_2018.merge(diverstanding_data,on='raceId').dropna()
data_2018 = data_2018.merge(drivers_data,on='driverId').dropna()
data_2018.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2018 = data_2018.sort_values(['round','position'])
data_2018
drivers = data_2018['code'].unique()


fig29 = go.Figure()
fig30 = go.Figure()
for i in drivers:
    d = data_2018.where(data_2018['code'] == i).dropna().sort_values(by='round') 
    fig29.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig30.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig29.show()


fig30['layout']['yaxis']['autorange'] = "reversed"
fig30.show()


data_teams_2018 = races_data.where(races_data['year']==2018).dropna()
data_teams_2018 = data_teams_2018.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2018 = data_teams_2018.merge(constructors_data,on='constructorId').dropna()
data_teams_2018.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2018 = data_teams_2018.sort_values(['round','position'])
constructors = data_teams_2018['name_y'].unique()
data_teams_2018


fig31 = go.Figure()
fig32 = go.Figure()

for i in constructors:
    d = data_teams_2018.where(data_teams_2018['name_y'] == i).dropna()
    fig31.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig32.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig31.show()


fig32['layout']['yaxis']['autorange'] = "reversed"
fig32.show()


data_2019 = races_data.where(races_data['year']==2019).dropna()
data_2019 = data_2019.merge(diverstanding_data,on='raceId').dropna()
data_2019 = data_2019.merge(drivers_data,on='driverId').dropna()
data_2019.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2019 = data_2019.sort_values(['round','position'])
data_2019
drivers = data_2019['code'].unique()


fig33 = go.Figure()
fig34 = go.Figure()
for i in drivers:
    d = data_2019.where(data_2019['code'] == i).dropna().sort_values(by='round') 
    fig33.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig34.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig33.show()


fig34['layout']['yaxis']['autorange'] = "reversed"
fig34.show()


data_teams_2019 = races_data.where(races_data['year']==2019).dropna()
data_teams_2019 = data_teams_2019.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2019 = data_teams_2019.merge(constructors_data,on='constructorId').dropna()
data_teams_2019.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2019 = data_teams_2019.sort_values(['round','position'])
constructors = data_teams_2019['name_y'].unique()
data_teams_2019


fig35 = go.Figure()
fig36 = go.Figure()

for i in constructors:
    d = data_teams_2019.where(data_teams_2019['name_y'] == i).dropna()
    fig35.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig36.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig35.show()


fig36['layout']['yaxis']['autorange'] = "reversed"
fig36.show()


data_2020 = races_data.where(races_data['year']==2020).dropna()
data_2020 = data_2020.merge(diverstanding_data,on='raceId').dropna()
data_2020 = data_2020.merge(drivers_data,on='driverId').dropna()
data_2020.drop(columns=['date','time','url_x','url_y','driverStandingsId','driverId','dob','number','nationality','raceId'],inplace=True)
data_2020 = data_2020.sort_values(['round','position'])
data_2020
drivers = data_2020['code'].unique()


fig37 = go.Figure()
fig38 = go.Figure()
for i in drivers:
    d = data_2020.where(data_2020['code'] == i).dropna().sort_values(by='round') 
    fig37.add_traces(go.Scatter(x=d['name'],y=d['points'],name=i,mode='lines+markers'))
    fig38.add_traces(go.Scatter(x=d['name'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig37.show()


fig38['layout']['yaxis']['autorange'] = "reversed"
fig38.show()


data_teams_2020 = races_data.where(races_data['year']==2020).dropna()
data_teams_2020 = data_teams_2020.merge(constructorstanding_data,on='raceId').dropna()
data_teams_2020 = data_teams_2020.merge(constructors_data,on='constructorId').dropna()
data_teams_2020.drop(columns=['date','time','url_x','url_y','constructorStandingsId','constructorId','nationality','raceId'],inplace=True)
data_teams_2020 = data_teams_2020.sort_values(['round','position'])
constructors = data_teams_2020['name_y'].unique()
data_teams_2020


fig39 = go.Figure()
fig40 = go.Figure()

for i in constructors:
    d = data_teams_2020.where(data_teams_2020['name_y'] == i).dropna()
    fig39.add_trace(go.Scatter(x=d['name_x'],y=d['points'],name=i,mode='lines+markers'))
    fig40.add_trace(go.Scatter(x=d['name_x'],y=d['position'],name=i,mode='lines+markers',hovertext=d.points))
fig39.show()


fig40['layout']['yaxis']['autorange'] = "reversed"
fig40.show()

	year	round	circuitId	name	points	position	positionText	wins	driverRef	code	forename	surname
361	2011.0	1.0	1.0	Australian Grand Prix	25.0	1	1	1	vettel	VET	Sebastian	Vettel
342	2011.0	1.0	1.0	Australian Grand Prix	18.0	2	2	0	hamilton	HAM	Lewis	Hamilton
323	2011.0	1.0	1.0	Australian Grand Prix	15.0	3	3	0	petrov	PET	Vitaly	Petrov
304	2011.0	1.0	1.0	Australian Grand Prix	12.0	4	4	0	alonso	ALO	Fernando	Alonso
285	2011.0	1.0	1.0	Australian Grand Prix	10.0	5	5	0	webber	WEB	Mark	Webber
...	...	...	...	...	...	...	...	...	...	...	...	...
132	2011.0	19.0	18.0	Brazilian Grand Prix	0.0	24	24	0	ambrosio	DAM	Jérôme	d'Ambrosio
113	2011.0	19.0	18.0	Brazilian Grand Prix	0.0	25	25	0	glock	GLO	Timo	Glock
451	2011.0	19.0	18.0	Brazilian Grand Prix	0.0	26	26	0	karthikeyan	KAR	Narain	Karthikeyan
475	2011.0	19.0	18.0	Brazilian Grand Prix	0.0	27	27	0	ricciardo	RIC	Daniel	Ricciardo
485	2011.0	19.0	18.0	Brazilian Grand Prix	0.0	28	28	0	chandhok	CHA	Karun	Chandhok

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
171	2011.0	1.0	1.0	Australian Grand Prix	35.0	1	1	1	red_bull	Red Bull
152	2011.0	1.0	1.0	Australian Grand Prix	26.0	2	2	0	mclaren	McLaren
114	2011.0	1.0	1.0	Australian Grand Prix	18.0	3	3	0	ferrari	Ferrari
133	2011.0	1.0	1.0	Australian Grand Prix	15.0	4	4	0	renault	Renault
95	2011.0	1.0	1.0	Australian Grand Prix	4.0	5	5	0	toro_rosso	Toro Rosso
76	2011.0	1.0	1.0	Australian Grand Prix	3.0	6	6	0	force_india	Force India
57	2011.0	1.0	1.0	Australian Grand Prix	0.0	7	7	0	lotus_racing	Lotus
38	2011.0	1.0	1.0	Australian Grand Prix	0.0	8	8	0	virgin	Virgin
19	2011.0	1.0	1.0	Australian Grand Prix	0.0	9	9	0	williams	Williams
0	2011.0	1.0	1.0	Australian Grand Prix	0.0	10	10	0	mercedes	Mercedes
172	2011.0	2.0	2.0	Malaysian Grand Prix	72.0	1	1	2	red_bull	Red Bull
153	2011.0	2.0	2.0	Malaysian Grand Prix	48.0	2	2	0	mclaren	McLaren
115	2011.0	2.0	2.0	Malaysian Grand Prix	36.0	3	3	0	ferrari	Ferrari
134	2011.0	2.0	2.0	Malaysian Grand Prix	30.0	4	4	0	renault	Renault
190	2011.0	2.0	2.0	Malaysian Grand Prix	6.0	5	5	0	sauber	Sauber
96	2011.0	2.0	2.0	Malaysian Grand Prix	4.0	6	6	0	toro_rosso	Toro Rosso
77	2011.0	2.0	2.0	Malaysian Grand Prix	4.0	7	7	0	force_india	Force India
1	2011.0	2.0	2.0	Malaysian Grand Prix	2.0	8	8	0	mercedes	Mercedes
58	2011.0	2.0	2.0	Malaysian Grand Prix	0.0	9	9	0	lotus_racing	Lotus
39	2011.0	2.0	2.0	Malaysian Grand Prix	0.0	10	10	0	virgin	Virgin
20	2011.0	2.0	2.0	Malaysian Grand Prix	0.0	11	11	0	williams	Williams
208	2011.0	2.0	2.0	Malaysian Grand Prix	0.0	12	12	0	hrt	HRT
173	2011.0	3.0	17.0	Chinese Grand Prix	105.0	1	1	2	red_bull	Red Bull
154	2011.0	3.0	17.0	Chinese Grand Prix	85.0	2	2	1	mclaren	McLaren
116	2011.0	3.0	17.0	Chinese Grand Prix	50.0	3	3	0	ferrari	Ferrari
135	2011.0	3.0	17.0	Chinese Grand Prix	32.0	4	4	0	renault	Renault
2	2011.0	3.0	17.0	Chinese Grand Prix	16.0	5	5	0	mercedes	Mercedes
191	2011.0	3.0	17.0	Chinese Grand Prix	7.0	6	6	0	sauber	Sauber
97	2011.0	3.0	17.0	Chinese Grand Prix	4.0	7	7	0	toro_rosso	Toro Rosso
78	2011.0	3.0	17.0	Chinese Grand Prix	4.0	8	8	0	force_india	Force India

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
40	2012.0	1.0	1.0	Australian Grand Prix	40.0	1	1	1	mclaren	McLaren
200	2012.0	1.0	1.0	Australian Grand Prix	30.0	2	2	0	red_bull	Red Bull
160	2012.0	1.0	1.0	Australian Grand Prix	12.0	3	3	0	sauber	Sauber
180	2012.0	1.0	1.0	Australian Grand Prix	10.0	4	4	0	ferrari	Ferrari
140	2012.0	1.0	1.0	Australian Grand Prix	6.0	5	5	0	lotus_f1	Lotus F1
...	...	...	...	...	...	...	...	...	...	...
79	2012.0	20.0	18.0	Brazilian Grand Prix	76.0	8	8	1	williams	Williams
139	2012.0	20.0	18.0	Brazilian Grand Prix	26.0	9	9	0	toro_rosso	Toro Rosso
39	2012.0	20.0	18.0	Brazilian Grand Prix	0.0	10	10	0	caterham	Caterham
19	2012.0	20.0	18.0	Brazilian Grand Prix	0.0	11	11	0	marussia	Marussia
238	2012.0	20.0	18.0	Brazilian Grand Prix	0.0	12	12	0	hrt	HRT

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
19	2013.0	1.0	1.0	Australian Grand Prix	30.0	1	1	0	ferrari	Ferrari
0	2013.0	1.0	1.0	Australian Grand Prix	26.0	2	2	1	lotus_f1	Lotus F1
38	2013.0	1.0	1.0	Australian Grand Prix	23.0	3	3	0	red_bull	Red Bull
57	2013.0	1.0	1.0	Australian Grand Prix	10.0	4	4	0	mercedes	Mercedes
76	2013.0	1.0	1.0	Australian Grand Prix	10.0	5	5	0	force_india	Force India
...	...	...	...	...	...	...	...	...	...	...
151	2013.0	19.0	18.0	Brazilian Grand Prix	57.0	7	7	0	sauber	Sauber
132	2013.0	19.0	18.0	Brazilian Grand Prix	33.0	8	8	0	toro_rosso	Toro Rosso
170	2013.0	19.0	18.0	Brazilian Grand Prix	5.0	9	9	0	williams	Williams
189	2013.0	19.0	18.0	Brazilian Grand Prix	0.0	10	10	0	marussia	Marussia
208	2013.0	19.0	18.0	Brazilian Grand Prix	0.0	11	11	0	caterham	Caterham

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
171	2014.0	1.0	1.0	Australian Grand Prix	33.0	1	1	0	mclaren	McLaren
190	2014.0	1.0	1.0	Australian Grand Prix	25.0	2	2	1	mercedes	Mercedes
152	2014.0	1.0	1.0	Australian Grand Prix	18.0	3	3	0	ferrari	Ferrari
133	2014.0	1.0	1.0	Australian Grand Prix	10.0	4	4	0	williams	Williams
114	2014.0	1.0	1.0	Australian Grand Prix	9.0	5	5	0	force_india	Force India
...	...	...	...	...	...	...	...	...	...	...
113	2014.0	19.0	24.0	Abu Dhabi Grand Prix	30.0	7	7	0	toro_rosso	Toro Rosso
75	2014.0	19.0	24.0	Abu Dhabi Grand Prix	10.0	8	8	0	lotus_f1	Lotus F1
56	2014.0	19.0	24.0	Abu Dhabi Grand Prix	2.0	9	9	0	marussia	Marussia
94	2014.0	19.0	24.0	Abu Dhabi Grand Prix	0.0	10	10	0	sauber	Sauber
37	2014.0	19.0	24.0	Abu Dhabi Grand Prix	0.0	11	11	0	caterham	Caterham

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
5	2015.0	1.0	1.0	Australian Grand Prix	43.0	1	1	1	mercedes	Mercedes
24	2015.0	1.0	1.0	Australian Grand Prix	15.0	2	2	0	ferrari	Ferrari
62	2015.0	1.0	1.0	Australian Grand Prix	14.0	3	3	0	sauber	Sauber
43	2015.0	1.0	1.0	Australian Grand Prix	12.0	4	4	0	williams	Williams
81	2015.0	1.0	1.0	Australian Grand Prix	8.0	5	5	0	red_bull	Red Bull
...	...	...	...	...	...	...	...	...	...	...
170	2015.0	19.0	24.0	Abu Dhabi Grand Prix	78.0	6	6	0	lotus_f1	Lotus F1
132	2015.0	19.0	24.0	Abu Dhabi Grand Prix	67.0	7	7	0	toro_rosso	Toro Rosso
75	2015.0	19.0	24.0	Abu Dhabi Grand Prix	36.0	8	8	0	sauber	Sauber
151	2015.0	19.0	24.0	Abu Dhabi Grand Prix	27.0	9	9	0	mclaren	McLaren
188	2015.0	19.0	24.0	Abu Dhabi Grand Prix	0.0	10	10	0	manor	Manor Marussia

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
42	2016.0	1.0	1.0	Australian Grand Prix	43.0	1	1	1	mercedes	Mercedes
210	2016.0	1.0	1.0	Australian Grand Prix	15.0	2	2	0	ferrari	Ferrari
168	2016.0	1.0	1.0	Australian Grand Prix	14.0	3	3	0	williams	Williams
189	2016.0	1.0	1.0	Australian Grand Prix	12.0	4	4	0	red_bull	Red Bull
147	2016.0	1.0	1.0	Australian Grand Prix	8.0	5	5	0	haas	Haas F1 Team
...	...	...	...	...	...	...	...	...	...	...
125	2016.0	21.0	24.0	Abu Dhabi Grand Prix	63.0	7	7	0	toro_rosso	Toro Rosso
167	2016.0	21.0	24.0	Abu Dhabi Grand Prix	29.0	8	8	0	haas	Haas F1 Team
104	2016.0	21.0	24.0	Abu Dhabi Grand Prix	8.0	9	9	0	renault	Renault
20	2016.0	21.0	24.0	Abu Dhabi Grand Prix	2.0	10	10	0	sauber	Sauber
41	2016.0	21.0	24.0	Abu Dhabi Grand Prix	1.0	11	11	0	manor	Manor Marussia

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
0	2017.0	1.0	1.0	Australian Grand Prix	37.0	1	1	1	ferrari	Ferrari
20	2017.0	1.0	1.0	Australian Grand Prix	33.0	2	2	0	mercedes	Mercedes
40	2017.0	1.0	1.0	Australian Grand Prix	10.0	3	3	0	red_bull	Red Bull
60	2017.0	1.0	1.0	Australian Grand Prix	8.0	4	4	0	williams	Williams
80	2017.0	1.0	1.0	Australian Grand Prix	7.0	5	5	0	force_india	Force India
...	...	...	...	...	...	...	...	...	...	...
139	2017.0	20.0	24.0	Abu Dhabi Grand Prix	57.0	6	6	0	renault	Renault
119	2017.0	20.0	24.0	Abu Dhabi Grand Prix	53.0	7	7	0	toro_rosso	Toro Rosso
199	2017.0	20.0	24.0	Abu Dhabi Grand Prix	47.0	8	8	0	haas	Haas F1 Team
179	2017.0	20.0	24.0	Abu Dhabi Grand Prix	30.0	9	9	0	mclaren	McLaren
159	2017.0	20.0	24.0	Abu Dhabi Grand Prix	5.0	10	10	0	sauber	Sauber

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
0	2018.0	1.0	1.0	Australian Grand Prix	40.0	1	1	1	ferrari	Ferrari
21	2018.0	1.0	1.0	Australian Grand Prix	22.0	2	2	0	mercedes	Mercedes
42	2018.0	1.0	1.0	Australian Grand Prix	20.0	3	3	0	red_bull	Red Bull
63	2018.0	1.0	1.0	Australian Grand Prix	12.0	4	4	0	mclaren	McLaren
84	2018.0	1.0	1.0	Australian Grand Prix	7.0	5	5	0	renault	Renault
...	...	...	...	...	...	...	...	...	...	...
83	2018.0	21.0	24.0	Abu Dhabi Grand Prix	62.0	6	6	0	mclaren	McLaren
125	2018.0	21.0	24.0	Abu Dhabi Grand Prix	52.0	7	7	0	force_india	Force India
146	2018.0	21.0	24.0	Abu Dhabi Grand Prix	48.0	8	8	0	sauber	Sauber
188	2018.0	21.0	24.0	Abu Dhabi Grand Prix	33.0	9	9	0	toro_rosso	Toro Rosso
167	2018.0	21.0	24.0	Abu Dhabi Grand Prix	7.0	10	10	0	williams	Williams

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
84	2019.0	1.0	1.0	Australian Grand Prix	44.0	1	1	1	mercedes	Mercedes
42	2019.0	1.0	1.0	Australian Grand Prix	22.0	2	2	0	ferrari	Ferrari
63	2019.0	1.0	1.0	Australian Grand Prix	15.0	3	3	0	red_bull	Red Bull
21	2019.0	1.0	1.0	Australian Grand Prix	8.0	4	4	0	haas	Haas F1 Team
0	2019.0	1.0	1.0	Australian Grand Prix	6.0	5	5	0	renault	Renault
...	...	...	...	...	...	...	...	...	...	...
167	2019.0	21.0	24.0	Abu Dhabi Grand Prix	85.0	6	6	0	toro_rosso	Toro Rosso
146	2019.0	21.0	24.0	Abu Dhabi Grand Prix	73.0	7	7	0	racing_point	Racing Point
125	2019.0	21.0	24.0	Abu Dhabi Grand Prix	57.0	8	8	0	alfa	Alfa Romeo
41	2019.0	21.0	24.0	Abu Dhabi Grand Prix	28.0	9	9	0	haas	Haas F1 Team
209	2019.0	21.0	24.0	Abu Dhabi Grand Prix	1.0	10	10	0	williams	Williams

	year	round	circuitId	name_x	points	position	positionText	wins	constructorRef	name_y
153	2020.0	1.0	70.0	Austrian Grand Prix	37.0	1	1	1	mercedes	Mercedes
119	2020.0	1.0	70.0	Austrian Grand Prix	26.0	2	2	0	mclaren	McLaren
136	2020.0	1.0	70.0	Austrian Grand Prix	19.0	3	3	0	ferrari	Ferrari
102	2020.0	1.0	70.0	Austrian Grand Prix	8.0	4	4	0	racing_point	Racing Point
85	2020.0	1.0	70.0	Austrian Grand Prix	6.0	5	5	0	alphatauri	AlphaTauri
...	...	...	...	...	...	...	...	...	...	...
152	2020.0	17.0	24.0	Abu Dhabi Grand Prix	131.0	6	6	0	ferrari	Ferrari
101	2020.0	17.0	24.0	Abu Dhabi Grand Prix	107.0	7	7	1	alphatauri	AlphaTauri
67	2020.0	17.0	24.0	Abu Dhabi Grand Prix	8.0	8	8	0	alfa	Alfa Romeo
16	2020.0	17.0	24.0	Abu Dhabi Grand Prix	3.0	9	9	0	haas	Haas F1 Team
50	2020.0	17.0	24.0	Abu Dhabi Grand Prix	0.0	10	10	0	williams	Williams